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SUMMARY:Linear Separability of Gene Expression Dataset - Dr. Benny Chor (U
 niversity of Tel-Aviv)
DTSTART:20061018T130000Z
DTEND:20061018T140000Z
UID:TALK5714@talks.cam.ac.uk
CONTACT:Danielle Stretch
DESCRIPTION:We examine simple geometric properties of gene expression data
 sets\, where samples are taken from two distinct classes (e.g. two types o
 f cancer). Specifically\, the problem of linear separability for pairs of 
 genes is investigated. We developed and implemented novel\, highly efficie
 nt algorithmic tools for finding all pairs of genes that induce a linear s
 eparation of the two sample classes. These tools are based on computationa
 l geometric properties\, and were applied to ten publicly available cancer
  datasets. We discovered that seven out of the ten datasets examined are h
 ighly separable. Statistically\, this phenomenon is highly significant\, a
 nd is very unlikely to occur at random.\n
LOCATION:MR5\, DAMTP
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